An ABS control logic based on wheel force measurement

The paper presents an anti-lock braking system (ABS) control logic based on the measurement of the longitudinal forces at the hub bearings. The availability of force information allows to design a logic that does not rely on the estimation of the tyre-road friction coefficient, since it continuously tries to exploit the maximum longitudinal tyre force. The logic is designed by means of computer simulation and then tested on a specific hardware in the loop test bench: the experimental results confirm that measured wheel force can lead to a significant improvement of the ABS performances in terms of stopping distance also in the presence of road with variable friction coefficien

An optimized anti-lock braking system in the presence of multiple road surface types

In this paper, the use of adaptive anti-lock braking system (A-ABS) comprising of a road surface identification (RSID) system and road surface information modules is presented. The proposed ABS system is capable of identifying and differentiating different types of road surfaces, and applying an amount of brake force appropriate to the road surface type being encountered in order to prevent wheel lockup as well as to minimize the braking distance. A discriminative hierarchical evolutionary fuzzy system learns and identifies on-the-fly the road surface characteristics, from a set of built-in road surface information modules, in a closed-loop adaptive configuration. The closed-loop nature of RSID allows the system to adapt and respond very fast to a sudden change in surface condition. In order to verify the performance of the proposal, simulation results obtained from cars equipped with A-ABS, a reference ABS, and a non-ABS are provided and discussed

Sensitivity-Based Hierarchical Controller Architectures for Active Suspension

In this brief, a sensitivity analysis of hierarchical fuzzy system (HFS) is conducted, allowing a sensitivity order of controller inputs to be established and used in a hierarchical fuzzy system inputs placement process. The frequency response analysis method is used to analyze the sensitivity of the controller output with respect to perturbation. This sensitivity knowledge thus constitutes a platform on which three different designs of HFS are investigated. An automotive active suspension system is chosen as an application for the HFS models for performance verification purposes. The simulation results are provided and discussed in this brief

Structure adaptation of hierarchical knowledge-based classifiers

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Hierarchical and Interpretable Connectionist Structure Generation from Data

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Structure adaptation of hierarchical knowledge-based classifiers

This paper introduces a new method to identify the qualified rule-relevant nodes to construct hierarchical neuro-fuzzy systems (HNFSs). After learning, the proposed method analyzes the entire history of activities and behaviors of all rule nodes, which reflects their levels of involvement or contribution during the process. The less qualified rule-relevant nodes can then be identified and removed, reducing the size and complexity of the HNFS. Upon the repetitive learning process, the method may be repetitively applied until a satisfactory result is obtained, simultaneously improving the performance and reducing the size and complexity. Incorporated with the method is a new HNFS architecture which addresses both the scalability problem experienced in rule based systems and the restriction of the ‘‘overcrowded defuzzification’’ problem found in hierarchical designs. In order to verify the performance, the proposed method has been successfully tested against five well-known classification problems whose results are provided and then discussed in the concluding remarks